中圖分類號： TP18
文獻標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2020.06.002
引用格式： 林通，陳新，唐曉，等. 基于雙流卷積神經(jīng)網(wǎng)絡(luò)和生成式對抗網(wǎng)絡(luò)的行人重識別算法[J].信息技術(shù)與網(wǎng)絡(luò)安全，2020，39(6)：7-12.

Dual stream ConvNet-Gan for person re-identification

Abstract： The performance of current algorithm of person re-identification(ReID) has been made great progress, but there are still a lot of difficults in dealing with different backgrounds and poses. In order to cope with the difficult in actual scenes, this paper proposes a novel model, which can very well solve the problem that the characters have different poses and the number of pictures in the dataset is insufficient and uneven. Firstly the IDs with a relatively small number of images are screened out to expand by transferring pose and solve the problem of imbalanced datasets. Then two different convolutional neural networks are used to extract picture features and they are joined to get different characteristics. In the end, the task with extracted features is completed. This model solves the impact of different poses on the recognition effect very well. At the same time, it uses two independent feature extraction networks to extract the features of the person more comprehensively. Experimental results confirm that this model significantly improves performance and achieves high accuracy in Market-1501 and DukeMTMC-Reid. When tested on the DukeMTMC-Reid dataset, Rank-1 was increased to 92.10%, and mAP was increased to 84.60%.

Key words : person ReID；CNN；generative adversarial network；pose transfer